Handbook in Monte Carlo Simulation

Paolo Brandimarte 지음

Wiley 2014년 06월 17일 출간

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An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economicsProviding readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization.The Handbook in Monte Carlo Simulation features:An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentialsCarefully crafted examples in order to spot potential pitfalls and drawbacks of each approachAn accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methodsNumerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentationThe Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.

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Preface xiiiPart I Overview and Motivation1 Introduction to Monte Carlo Methods 31.1 Historical origin of Monte Carlo simulation 41.2 Monte Carlo Simulation vs. Monte Carlo Sampling 71.3 System dynamics and the mechanics of Monte Carlo simulation 101.4 Simulation and optimization 211.5 Pitfalls in Monte Carlo simulation 301.6 Software tools for Monte Carlo simulation 351.7 Prerequisites 37For further reading 38Chapter References 382 Numerical Integration Methods 412.1 Classical quadrature formulae 432.2 Gaussian quadrature 482.3 Extension to higher dimensions: Product rules 532.4 Alternative approaches for high-dimensional integration 552.5 Relationship with moment matching 672.6 Numerical integration in R 69For further reading 71Chapter References 71Part II Input Analysis: Modeling and Estimation3 Stochastic Modeling in Finance and Economics 753.1 Introductory examples 773.2 Some common probability distributions 863.3 Multivariate distributions: Covariance and correlation 1113.4 Modeling dependence with copulae 1273.5 Linear regression models: a probabilistic view 1363.6 Time series models 1373.7 Stochastic differential equations 1583.8 Dimensionality reduction 177S3.1 Risk-neutral derivative pricing 190S3.1.1 Option pricing in the binomial model 192S3.1.2 A continuous-time model for option pricing: The Black&n-Scholes&n-Merton formula 194S3.1.3 Option pricing in incomplete markets 199For further reading 202Chapter References 2034 Estimation and Fitting 2054.1 Basic inferential statistics in R 2074.2 Parameter estimation 2154.3 Checking the fit of hypothetical distributions 2244.4 Estimation of linear re

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저자(글) Paolo Brandimarte

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PAOLO BRANDIMARTE is Full Professor of Quantitative Methods for Finance and Logistics in the Department of Mathematical Sciences at Politecnico di Torino in Italy. He has extensive teaching experience in engineering and economics faculties, including master#x2019;s- and PhD-level courses. Dr. Brandimarte is the author or coauthor of Introduction to Distribution Logistics, Quantitative Methods: An Introduction for Business Management, and Numerical Methods in Finance and Economics: A MATLAB-Based Introduction, Second Edition, all published by Wiley.